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Ethane is the most abundant non-methane hydrocarbon in the Earth’s atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns. As with many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model, we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above (we provide R code for all proposed methods on https://www.stephansmeekes.nl/code.)., Horizon 2020 https://doi.org/10.13039/501100007601, Fonds De La Recherche Scientifique - FNRS https://doi.org/10.13039/501100002661

Corresponding publicationsWorking Paper | Generating Univariate Fractional Integration within a Large VAR(1) 〈halshs-01944588〉; International audience; This paper shows that a large dimensional vector autoregressive model (VAR) of finite order can generate fractional integration in the marginalized univariate series. We derive high-level assumptions under which the final equation representation of a VAR(1) leads to univariate fractional white noises and verify the validity of these assumptions for two specific models.

This paper studies Davis–Maschler reduced games of two-bound core games and shows that all these reduced games with respect to core elements are two-bound core games with the same pair of bounds. Based on associated reduced game properties, we axiomatically characterize the core, the nucleolus, and the egalitarian core for two-bound core games. Moreover, we show that the egalitarian core for two-bound core games is single-valued and we provide an explicit expression.

ACL-2; International audience; In this paper, we study the measurement of polarization in collective decision making problems with ordinal preferences over alternatives. We argue that polarization can be measured as an aggregation of antagonisms over pairs of alternatives in the society. We propose a measure of this sort and show that it is the only measure satisfying some normatively appealing conditions.

This paper develops a unified analysis of the impacts of production delays on aggregate price fluctuations in a continuous-time cobweb-type model. We find that the time inconsistency between demand and supply due to production delays inherently generates an overshooting effect in prices. Large production delays give rise to price fluctuations, even with a low intensity of choice. We also provide results consistent with the rational route to randomness of Brock and Hommes (1997) in a continuous-time infinite-dimensional economy.

In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by introducing a new learning technique called curriculum learning. With curriculum learning, we gradually increase the complexity of the environment and we find that it has a positive effect on the expected reward of a traversal. We see that the number of training steps needed for the two quantum approaches is lower than that needed for the classical approach.

Matching electricity demand and supply by decentralised generators will be very important in the near future, where more and more electricity has to be produced sustainably. Optimisation problems that address this problem often have quadratic objective functions or constraints, due to the quadratic nature of energy loss. Where classical solvers struggle with quadratic, often integer or binary, optimisation problems, quantum computing (inspired) solvers are well equipped for this kind of problems. In this paper, we present such an optimisation problem and the performance of the quantum annealer in solving this problem, in comparison with classical methods and commercial solvers. We show that the hybrid, quantum-classical, solvers provided by D-Wave can outperform classical solvers despite the small-scale of the available quantum hardware.

We propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

We conduct a field and an online classroom experiment to study gender differences in self-set performance goals and their effects on performance in a real-effort task. We distinguish between public and private goals, performance being public and identifiable in both cases. Participants set significantly more ambitious goals when these are public. Women choose lower goals than men in both treatments. Men perform better than women under private and public goals as well as in the absence of goal setting, consistent with the identifiability of performance causing gender differences, as found in other studies. Compared to private goal setting, public goal setting does not affect men's performance at all but it leads to women's performance being significantly lower. Comparing self-set goals with actual performance we find that under private goal setting women's performance is on average 67% of goals, whereas for men it is 57%. Under public goal setting the corresponding percentages are 43% and 39%, respectively., Brandts thanks the Spanish Ministerio de Ciencia e Innovación (Grant number PID2020-114251GB-I00), the Spanish Agencia Estatal de Investigación (AEI), through the Severo Ochoa Programme for Centres of Excellence in R&D (Barcelona School of Economics CEX2019-000915-S) and the Generalitat de Catalunya (Grant: 2017 SGR 1136) for financial support.

International audience; We study repeated zero-sum games where one of the players pays a certain cost each time he changes his action. We derive the properties of the value and optimal strategies as a function of the ratio between the switching costs and the stage payoffs. In particular, the strategies exhibit a robustness property and typically do not change with a small perturbation of this ratio. Our analysis extends partially to the case where the players are limited to simpler strategies that are history independent―namely, static strategies. In this case, we also characterize the (minimax) value and the strategies for obtaining it.

In this paper we investigate to what extent the bootstrap can be applied to conditional mean models, such as regression or time series models, when the volatility of the innovations is random and possibly non-stationary. In fact, the volatility of many economic and financial time series displays persistent changes and possible non-stationarity. However, the theory of the bootstrap for such models has focused on deterministic changes of the unconditional variance and little is known about the performance and the validity of the bootstrap when the volatility is driven by a non-stationary stochastic process. This includes near-integrated exogenous volatility processes as well as near-integrated GARCH processes, where the conditional variance has a diffusion limit; a further important example is the case where volatility exhibits infrequent jumps. This paper fills this gap in the literature by developing conditions for bootstrap validity in time series and regression models with non-stationary, stochastic volatility. We show that in such cases the distribution of bootstrap statistics (conditional on the data) is random in the limit. Consequently, the conventional approaches to proofs of bootstrap consistency, based on the notion of weak convergence in probability of the bootstrap statistic, fail to deliver the required validity results. Instead, we use the concept of ‘weak convergence in distribution’ to develop and establish novel conditions for validity of the wild bootstrap, conditional on the volatility process. We apply our results to several testing problems in the presence of non-stationary stochastic volatility, including testing in a location model, testing for structural change using CUSUM-type functionals, and testing for a unit root in autoregressive models. Importantly, we work under sufficient conditions for bootstrap validity that include the absence of statistical leverage effects, i.e., correlation between the error process and its future conditional variance. The results of the paper are illustrated using Monte Carlo simulations, which indicate that a wild bootstrap approach leads to size control even in small samples., This research was supported by the Danish Council for Independent Research, Denmark (DSF Grant 015-00028B), by the University of Bologna, Italy (ALMA IDEA 2017 Grant), and by the Italian Ministry of University and Research, Italy (PRIN 2017 Grant 2017TA7TYC).

This paper introduces the new class of two-bound core games, where the core can be described by a lower bound and an upper bound on the payoffs of the players. Many classes of games turn out to be two-bound core games. We show that the core of each two-bound core game can be described equivalently by the pair of exact core bounds, and study to what extent the exact core bounds can be stretched while retaining the core description. We provide explicit expressions of the nucleolus for two-bound core games in terms of all pairs of bounds describing the core, using the Talmud rule for bankruptcy problems, and study to what extent these expressions are robust against game changes.

This paper introduces and studies a random arrival rule for bankruptcy problems with nontransferable utility. This bankruptcy rule generalizes the random arrival rule for bankruptcy problems with transferable utility which assigns the unique efficient allocation proportional to the sum of marginal vectors. We provide two axiomatic characterizations based on symmetry and monotonicity, respectively.

In this paper we extend to a dynamic setting the robust Orlicz premia and Haezendonck–Goovaerts risk measures introduced in Bellini, Laeven and Rosazza Gianin (2018). We extensively analyze the properties of the resulting dynamic risk measures. Furthermore, we characterize dynamic Orlicz premia that are time-consistent, and establish some relations between the time-consistency properties of dynamic robust Orlicz premia and the corresponding dynamic robust Haezendonck–Goovaerts risk measures.

Finitely many agents have preferences on a finite set of alternatives, single-peaked with respect to a connected graph with these alternatives as vertices. A probabilistic rule assigns to each preference profile a probability distribution over the alternatives. First, all unanimous and strategy-proof probabilistic rules are characterized when the graph is a tree. These rules are uniquely determined by their outcomes at those preference profiles at which all peaks are on leaves of the tree and, thus, extend the known case of a line graph. Second, it is shown that every unanimous and strategy-proof probabilistic rule is random dictatorial if and only if the graph has no leaves. Finally, the two results are combined to obtain a general characterization for every connected graph by using its block tree representation.

Using flexible Poisson regressions, we analyse a huge micro-level lifetime dataset from a Dutch pension fund, including categorical, continuous and spatial risk factors collected on participants in the fund. The availability of granular lifetime data allows us to quantify the longevity gap between the national population and the fund on the one hand, and between participants within the fund on the other hand. We identify the most important risk factors using statistical criteria that measure the in- and out-of-sample performance of the regression models. We evaluate the financial performance of the models by introducing a novel type of backtest, which selects the risk factors that contribute most to an accurate prediction of future pension liabilities. For this portfolio, the most relevant risk factors (next to age and gender) are the salary, the time spent in disability and working at irregular hours. The resulting personalized mortality risk profiles show substantial differences between the remaining life expectancies for the most-favourable and least-favourable risk profiles. Our method to estimate these longevity gaps will help policymakers to assess wanted and unwanted consequences of longevity risk sharing in pension schemes.

We analyze Nordhaus’ DICE model and show that the temperature and CO2 equations are needlessly complicated and can be simplified without loss of essence. In addition, we argue that the damage function can be altered in such a way that it lends itself to experiments involving extreme risk. We conclude that, within the philosophy of the DICE model, significant simplifications can be made which make the model more transparent, more robust, and easier to apply.

Among the existing estimators of factor-augmented regressions, the CCE approach is the most popular. A major reason for this popularity is the simplicity and good small-sample performance of the approach, making it very attractive from an empirical point of view. The main drawback is that most of the available asymptotic theory is based on quite restrictive assumptions, such as that the common factor component should be independent of the regressors. The present paper can be seen as a reaction to this. The purpose is to study the asymptotic properties of the pooled CCE estimator under more realistic conditions. In particular, the common factor component may be correlated with the regressors, and the true number of common factors, r , can be larger than the number of estimated factors, which in CCE is given by k + 1 , where k is the number of regressors. The main conclusion is that while the estimator is generally consistent, asymptotic normality can fail when r > k + 1 .

A mechanism allocates one unit of an infinitely divisible commodity among agents reporting a number between zero and one. Nash, Pareto optimal Nash, and strong equilibria are analyzed for the case where the agents have single-dipped preferences. One of the main results is that when the mechanism is anonymous, monotonic, standard, and order preserving, then the Pareto optimal Nash and strong equilibria coincide and assign Pareto optimal allocations that are characterized by so-called maximal coalitions: members of a maximal coalition prefer an equal coalition share over obtaining zero, whereas the outside agents prefer zero over obtaining an equal share from joining the coalition.

In the freight transportation industry, long-term contracts between logistics service providers (LSP) and shippers are common. These contracts typically contain long-term commitments related to, e.g., volume, lead time and price. The latter is mostly based on the expected cost of the transport mode that will be used for the transport of a shipment with the addition of a desired profit margin, i.e., cost-plus pricing. As these contracts apply over a longer period, e.g., a year, commitments need to be made long before the execution of the transport service itself. This long-term decision making contradicts with the fundamentals of synchromodality, which assume flexibility to change decisions with respect to route and transport mode in real time. In this research, we present an extensive numerical experiment that provides insight in the service quality characteristics, i.e., long-term commitments that have a significant impact on a LSP’s cost. In addition, interaction effects between long-term commitments of different shippers are expected, which should also be considered when analysing the cost implications. These results will serve as an input for a service-based pricing strategy for synchromodal transport. Such a strategy sets a price based on service quality characteristics of the transport, rather than the used transport mode. In this way, we aim to justify a contract price while the transport mode and route are still unknown.

We report the Atos Q-score for D-Wave's quantum devices, classical algorithms and hybrid quantum-classical solver. Computing the Q-score entails solving the Max-Cut problem for increasingly large graphs. This work presents the first computation of the Q-score on a quantum device and shows how these quantum devices compare to classical devices at solving optimisation problems. We use D-Wave's standard methods out of the box with a time limit of 60 seconds. The Q-score for D-Wave's 2000Q and Advantage devices are 70 and 140, respectively. The Q-score for two of D-Wave's classical algorithms, based on tabu search and simulated annealing respectively, are 2,300 and 5,800. Finally, we report the out-of-the-box hybrid approach to have a Q-score of 12,500., Comment: 8 pages, 6 figures. Submitted to IEEE QSW 2022

This paper develops a novel Method of Moments approach for panel data models with endogenous regressors and unobserved common factors. The proposed approach does not require estimating explicitly a large number of parameters in either time-series or cross-sectional dimension, T and N respectively. Hence, it is free from the incidental parameter problem. In particular, the proposed approach does not suffer from “Nickell bias” of order O(T−1), nor from bias terms that are of order O(N−1). Therefore, it can operate under substantially weaker restrictions compared to existing large T procedures. Two alternative GMM estimators are analyzed; one makes use of a fixed number of “averaged estimating equations” à la Anderson and Hsiao (1982), whereas the other one makes use of “stacked estimating equations”, the total number of which increases at the rate of O(T). It is demonstrated that both estimators are consistent and asymptotically mixed-normal as N→∞ for any value of T. Low-level conditions that ensure local and global identification in this setup are examined using several examples.

This paper introduces and studies a random arrival rule for bankruptcy problems with nontransferable utility. This bankruptcy rule generalizes the random arrival rule for bankruptcy problems with transferable utility which assigns the unique efficient allocation proportional to the sum of marginal vectors. We provide two axiomatic characterizations based on symmetry and monotonicity, respectively.

The Bowley solution refers to the optimal pricing density for the reinsurer and optimal ceded loss for the insurer when there is a monopolistic reinsurer. In a sequential game, the reinsurer first sets the pricing kernel, and thereafter the insurer selects the reinsurance contract given the pricing kernel. In this article, we study Bowley solutions under asymmetric information on the insurer's risk preferences where the identity of the insurer is unknown to the reinsurer. By assuming that the insurer adopts a Value-at-Risk measure or a convex distortion risk measure, the optimal pricing kernel for the insurer and the optimal ceded loss function for the reinsurer are determined. Numerical examples are presented to illustrate the results.

National audience; The New Keynesian theory of inflation determination is tested in this paper by means of laboratory experiments. We find that the Taylor principle is a necessary condition to ensure convergence to the inflation target, but it is not sufficient. Using a behavioral model of expectation formation, we show how heterogeneous expectations tend to self-organize on different forecasting strategies depending on monetary policy. Finally, we link the central bank’s ability to control inflation to the impact that monetary policy has on the type of feedback –positive or negative– between expectations and realizations of aggregate variables and in turn on the composition of subjects with respect to the type of forecasting rules they use.

We investigate competitive equilibria in a special type of incomplete markets, referred to as a comonotone market, where agents can only trade such that their risk allocation is comonotonic. The comonotone market is motivated by the no-sabotage condition. For instance, in a standard insurance market, the allocation of risk among the insured, the insurer and the reinsurers is assumed to be comonotonic a priori to the risk-exchange. Two popular classes of preferences in risk management and behavioral economics, dual utilities (DU) and rank-dependent expected utilities (RDU), are used to formulate agents’ objectives. We present various results on properties and characterization of competitive equilibria in this framework, and in particular their relation to complete markets. For DU-comonotone markets, we find the equilibrium in closed form and for RDU-comonotone markets, we find the equilibrium in closed form in special cases. The fundamental theorems of welfare economics are established in both the DU and RDU markets. We further propose an algorithm to numerically obtain competitive equilibria based on discretization, which works for both the DU-comonotone market and the RDU-comonotone market. Although the comonotone and complete markets are closely related, many of our findings are intriguing and in sharp contrast to results in the literature on complete markets in terms of existence, uniqueness, and closed-form solutions of the equilibria, and comonotonicity of the pricing kernel.

By analyzing the portfolio allocations of target date funds (TDFs), we document that the observed durations of TDF portfolios are inconsistent with the durations predicted by classical portfolio theory. We call this stylized fact the duration puzzle. We investigate to what extent several extensions of classical portfolio theory can explain the duration puzzle. More specifically, we consider the impact of human capital, inflation risk, and portfolio restrictions on the duration of the optimal portfolio. We find that it is difficult to explain the duration puzzle, especially for individuals aged between 35 and 65 years.

Accepted; International audience; Virtual age models are very useful to analyse recurrent events. Among the strengths of these models is their ability to account for treatment (or intervention) effects after an event occurrence. Despite their flexibility for modeling recurrent events, the number of applications is limited. This seems to be a result of the fact that in the semiparametric setting all the existing results assume the virtual age function that describes the treatment (or intervention) effects to be known. This shortcoming can be overcome by considering semiparametric virtual age models with parametrically specified virtual age functions. Yet, fitting such a model is a difficult task. Indeed, it has recently been shown that for these models the standard profile likelihood method fails to lead to consistent estimators. Here we show that consistent estimators can be constructed by smoothing the profile log-likelihood function appropriately. We show that our general result can be applied to most of the relevant virtual age models of the literature. Our approach shows that empirical process techniques may be a worthwhile alternative to martingale methods for studying asymptotic properties of these inference methods. A simulation study is provided to illustrate our consistency results together with an application to real data.

This paper studies Davis-Maschler reduced games of two-bound core games and shows that all these reduced games with respect to core elements are two-bound core games with the same pair of bounds. Based on associated reduced game properties, we axiomatically characterize the core, the nucleolus, and the egalitarian core for two-bound core games. Moreover, we show that the egalitarian core for two-bound core games is single-valued and we provide an explicit expression.

In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of classes are required to be both precise and interpretable. Linear modeling methods such as logistic regression are often adopted since they offer an acceptable balance between precision and interpretability. Linear methods, however, are not well equipped to handle categorical predictors with high cardinality or to exploit nonlinear relations in the data. As a solution, data preprocessing methods such as weight of evidence are typically used for transforming the predictors. The binning procedure that underlies the weight-of-evidence approach, however, has been little researched and typically relies on ad hoc or expert-driven procedures. The objective in this paper, therefore, is to propose a formalized, data-driven and powerful method. To this end, we explore the discretization of continuous variables through the binning of spline functions, which allows for capturing nonlinear effects in predictor variables and yields highly interpretable predictors that take only a small number of discrete values. Moreover, we extend the weight-of-evidence approach and propose to estimate the proportions using shrinkage estimators. Together, this method offers an improved ability to exploit both nonlinear and categorical predictors to achieve increased classification precision while maintaining the interpretability of the resulting model and decreasing the risk of overfitting. We present the results of a series of experiments in fraud detection and credit risk settings, which illustrate the effectiveness of the presented approach. (C) 2021 Elsevier B.V. All rights reserved.

We analyze the impact of short-run (90 days) and long-run (30 years) earthquake risk on real estate transaction prices in five Japanese cities (Tokyo, Osaka, Nagoya, Fukuoka, and Sapporo), using quarterly data over the period 2006–2015. We exploit a rich panel dataset (331,343 observations) with property characteristics, ward attractiveness information, macroeconomic variables, and long-run seismic hazard data, supplemented with short-run earthquake probabilities generated from a seismic excitation model using historical earthquake occurrences. We design a hedonic property price model that allows for subjective probability weighting, employ a multivariate error components structure, and develop associated maximum likelihood estimation and variance computation procedures. Our approach enables us to identify the total compensation for earthquake risk embedded in property prices, to decompose this into pieces stemming from short-run and long-run risk, and to distinguish between objective and subjectively weighted (“distorted”) earthquake probabilities. We find that objective long-run earthquake probabilities have a statistically significant negative impact on property prices, whereas short-run earthquake probabilities become statistically significant only when we allow them to be distorted. The total compensation for earthquake risk amounts to an average –2.0% of log property prices, slightly more than the annual income of a middle-income Japanese household. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

This paper considers the problem of identifying optimal locations for wireless service installations in smart cities. The problem is modelled as a facility location problem with multiple service types, known as the Multi Service Facility Location Problem (MSCFLP). Given a set of potential facility locations and demand point data, the goal is to identify at which locations the facilities should be opened, and which demand points should be serviced by each open facility in order to minimise costs. In this study, the demand quantities at each demand point are assumed to follow a probability distribution. An adaptive neighbourhood search heuristic is proposed in order to find a good solution to the problem, where the stochastic demand was translated to a deterministic capacity constraint. The heuristic iteratively improves the service allocations in sub-regions of the problem instances, starting from an initial feasible solution. The results show that the heuristic is able to find good solutions within very short time. Furthermore, we assessed the handling of the stochasticity by the model. Its performance is assessed by means of simulation, and results show that this approach works well in various scenarios of traffic models.

This book constitutes the refereed proceedings of the 22nd International Conference on Innovations for Community Services, I4CS 2022, held in Delft, The Netherlands, in June 2022. The 15 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 43 submissions. Three invited papers were also included in the volume. The papers focus on topics such as services for critical infrastructure; network architecture for communities; applications and services supporting work and life; community data and visualization; technology empowers industry processes; and future community support.

This chapter presents a dynamic model which explains output, employment and energy consumption in the French manufacturing sector in terms of the expected and actual path of wage rates and energy prices in units of output. The depreciation of the dollar, viewed with alarm by the European governments, helped the slowing down of inflation in Europe while it also led to a deterioration in the price and cost competitiveness and a decline in the profitability of European manufacturing. High and increasing unemployment has however contributed little to the moderation of inflation. In terms of consumer prices, inflation has remained stubbornly above 9 per cent, averaging 10 per cent for the 1973-1981 period. © 1985 Taylor and Francis. All rights reserved.

Cooperative games describe situations where players collaborate in coalitions and generate profits. Cooperative game theory analyses how to allocate profits generated by the grand coalition among the players in a fair way, and provides several significant solutions. This thesis introduces a new class of cooperative games, and studies explicit expressions of these solutions in this class. Moreover, these solutions are characterized using the axiomatic method. Next, this thesis focuses on non-cooperative games and mechanism design. Based on a sequential partition method, this thesis gives a new non-cooperative interpretation of the constrained equal awards rule for bankruptcy problems. Then, a particular mechanism is considered to solve division problems with single-dipped preferences. The Pareto optimal Nash and strong equilibria coincide and assign Pareto optimal allocations that are characterized by so-called maximal coalitions: non-involved agents prefer getting zero over an equal coalition share, whereas for agents in the coalition the opposite holds.

Recent progress in geophysics can be attributed to developments in heterogeneous HPC architectures, with one of the next major leaps being forecasted to be due to quantum computers. It is, however, very difficult to find the right combination of hardware, algorithms and a use-case. This is especially true for applications which have to be simultaneously: relevant and operating at scales where problems become difficult to solve using classical means. Maximizing stack-power for improved near surface characterization and velocity model building, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We present the quantum-native formulation of this problem. Furthermore, in order to improve the probability of success we embed it in a hybrid classical-quantum workflow. We present the results on controlled experiments run using a 5000-qubit machine and discuss the impact of different classical to quantum problem re-formulations.

This book introduces readers to the latest advances in synchromodal logistics and presents a framework for classifying various optimisation problems in this field. In turn, it explores how the framework can be used to solve a broad range of problems, such as those with and without a central decision-maker, those with and without full information, deterministic problems, problems involving uncertainty, and the optimisation of a full network design problem. It covers a variety of essential theoretical constructs, such as discrete optimisation, robust optimisation, optimisation under uncertainty, multi-objective optimisation and agent-based equilibrium models. Moreover, it includes practical elaborated use cases to deepen readers’ understanding. The book offers a good overview of the field of synchromodal optimisation problems for researchers and practitioners alike, together with practical modelling and problem-solving methods.

Quantum computing could be a potential game-changer in industry sectors relying on the efficient solutions of large-scale global optimization problems. Exploration geoscience, is full of optimization problems and hence is a good candidate for application of quantum computing. It was recently suggested that quantum annealing, a form of adiabatic quantum computer, is a much better suited quantum computing platform for optimization problems than gate-based quantum computing. In this work, we show how the residual statics estimation problem can be solved on the quantum annealer and present our first results obtained on a quantum computer.

This dissertation contributes to the econometric framework of macroeconomic models with heterogeneous agents. Heterogeneous agent models are used to study important questions related to the interaction of macroeconomics and inequality. However, where the economic literature is well-acquainted with econometric methods used for macroeconomic models with representative agents and the identification issues that one may encounter when trying to estimate these models, this is not the case for heterogeneous agent models. In heterogeneous agent models, as shown in the first part of this dissertation, new econometric challenges arise. For example, not all parameters are identifiable from aggregate variables, and to estimate them, additional information is needed, coming from cross-sectional or panel data. In response, this dissertation proposes a two-step identification and estimation procedure that can easily deal with these different data types and simplifies the identification analysis. The second and third part of this dissertation focus on a particular element in heterogeneous agent models, in particular, the earnings process that households in these models face. Because of the way heterogeneous agent macro models are solved, these earnings processes should have a discrete state space, yet most of the existing econometric literature focuses on continuous-support earnings processes. The second part of this dissertation proposes, identifies, and estimates a novel earnings process that features a rich notion of earnings risk heterogeneity, yet is discrete. In the last part, an improved method is proposed that can be used to discretize existing continuous-support stochastic processes.